Multispecies occupancy models estimate dependence among multiple species of interest from patterns of co-occurrence, but problems associated with separation and boundary estimates can lead to unreasonably large estimates of parameters and associated standard errors when species are rarely observed at the same site or when data are sparse. In this paper, we overcome these issues by implementing a penalized likelihood, which introduces a small bias in parameter estimates in exchange for a potentially large reduction in variance. We compare parameter estimates obtained from both penalized and unpenalized multispecies occupancy models fit to simulated data that exhibit various degrees of separation and to a real-word data set of bird surveys with little apparent overlap between potentially interacting species. Our simulation results demonstrate that penalized multispecies occupancy models did not exhibit boundary estimates and produced lower bias, lower mean squared error, and improved inference relative to unpenalized models. When applied to real-world data, our penalized multispecies occupancy model constrained boundary estimates and allowed for meaningful inference related to the interactions of two species of conservation concern. To facilitate the use of our penalized multispecies occupancy model, the techniques demonstrated in this paper have been integrated into the unmarked package in R programing language.

中文翻译：

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多物种占用模型的惩罚可能性提高了对物种相互作用的预测

多物种占有模型根据共现模式估计多个感兴趣物种之间的依赖性，但当物种在同一地点很少观察到或数据不准确时，与分离和边界估计相关的问题可能导致参数和相关标准误差的估计过大疏。在本文中，我们通过实施惩罚似然来克服这些问题，这在参数估计中引入了小偏差，以换取潜在的大幅减少的方差。我们比较了从受惩罚和未受惩罚的多物种占用模型获得的参数估计，这些模型适用于表现出不同程度分离的模拟数据和鸟类调查的真实数据集，在潜在相互作用的物种之间几乎没有明显的重叠。我们的模拟结果表明，受罚的多物种占用模型没有表现出边界估计，并且相对于未受罚的模型产生了较低的偏差、较低的均方误差和改进的推理。当应用于现实世界的数据时，我们的惩罚性多物种占用模型限制了边界估计，并允许对两种保护关注的物种的相互作用进行有意义的推断。为了便于使用我们的惩罚性多物种占用模型，本文中展示的技术已集成到 我们的惩罚性多物种占用模型限制了边界估计，并允许对两种保护关注的物种的相互作用进行有意义的推断。为了便于使用我们的惩罚性多物种占用模型，本文中展示的技术已集成到 我们的惩罚性多物种占用模型限制了边界估计，并允许对两种保护关注的物种的相互作用进行有意义的推断。为了便于使用我们的惩罚性多物种占用模型，本文中展示的技术已集成到R 编程语言中的未标记包。